NepaliBERT(Phase 1)

NEPALIBERT is a state-of-the-art language model for Nepali based on the BERT model. The model is trained using a masked language modeling (MLM).

Loading the model and tokenizer

1. clone the model repo

git lfs install
git clone https://huggingface.co/Rajan/NepaliBERT

2. Loading the Tokenizer

from transformers import BertTokenizer
vocab_file_dir = './NepaliBERT/' 
tokenizer = BertTokenizer.from_pretrained(vocab_file_dir,
                                        strip_accents=False,
                                         clean_text=False )

3. Loading the model:

from transformers import BertForMaskedLM
model = BertForMaskedLM.from_pretrained('./NepaliBERT')

The easiest way to check whether our language model is learning anything interesting is via the FillMaskPipeline.

Pipelines are simple wrappers around tokenizers and models, and the 'fill-mask' one will let you input a sequence containing a masked token (here, [mask]) and return a list of the most probable filled sequences, with their probabilities.

from transformers import pipeline

fill_mask = pipeline(
    "fill-mask",
    model=model,
    tokenizer=tokenizer
)

For more info visit the GITHUB🤗